wps4022.doc:10/4/06

BACKGROUND PAPER TO THE 2007 WORLD DEVELOPMENT REPORT

The Demography of Youth in Developing Countries and its Economic Implications

David Lam Department of Economics and Population Studies Center

University of Michigan Ann Arbor, MI 48106

davidl@umich.edu

World Bank Policy Research Working Paper 4022, October 2006 The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors, or the countries they represent. Policy Research Working Papers are available online at http://econ.worldbank.org.

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1. Introduction

Youth in developing countries find themselves in the midst of rapid social and economic

change. As documented in the recent National Academy of Sciences report on transitions to

adulthood in developing countries (Lloyd, 2005), young people face both new challenges and

new opportunities created by cultural and economic globalization. In order to fully understand

the situation of young people in developing countries today it is important to understand the rapid

demographic changes that produced the historically unprecedented numbers of young people in

the world today. These demographic changes potentially have important implications on the

labor market opportunities, access to public resources, and access to family resources for youth.

2. Data and Definitions

This paper uses the population estimates and projections provided by the United Nations

Population Division in World Population Prospects: The 2004 Revision Population Database.

Most of the analysis is based on the “medium variant” in the United Nations projections. A

detailed description of the assumptions used for these projections is provided in Appendix A. In

keeping with the definition adopted for the World Development Report 2007, the youth

population is defined as the population aged 12-24. In some cases, such as the comparison of

projections with and without HIV/AIDS, estimates are only available in five-year age groups. In

these cases the population aged 10-24 is used to represent the youth population. The countries

included in the analysis here include all of the countries classified as low-income to middle-

income by the World Bank, plus a selected group of Eastern European countries. In some cases

high-income countries are included for comparative purposes.

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3. The Peak in Youth Population

According to the population projections of the United Nations Population Division, the

number of young people in the world is close to reaching its historical peak, a peak that will

arguably be the largest number of young people the world will ever see. Figure 1 shows the

number of 12-24 year-olds in the world and in three major developing regions – Asia, Africa, and

Latin America – according to the U.N. medium variant projections. The population numbers

have been normalized to an index where 1950=100 for all regions in order to simplify

comparisons across regions. Although projections as far out as 2050 must be viewed with

caution, projections over the next two decades are relatively straightforward. The projections to

2050 are shown because they give a picture of at least one reasonable scenario for the trends that

will be experienced in the coming decades. As suggested by Figure 1, there is little question that

current youth cohorts are the largest that the world has ever seen, and that a period of rapid

growth in the size of these cohorts is coming to an end. While the world population aged 12-24

more than doubled in the 35 years between 1950 and 1985, a historically unprecedented rate of

growth, the world’s youth population is projected to decline in the 35 years after 2010.

As shown in Figure 1, the pattern for the world as a whole reflects wide diversity across

regions. With Asia accounting for almost two-thirds of the world population, the pattern for Asia

is very similar to the pattern for the world as a whole. The rapid approach of the peak in the

youth population is more clearly evident in Asia, however, with a projected peak around 2010.

Latin America and the Caribbean are also approaching a peak, with a broad leveling off that

begins around 2010. Figure 1 shows that the pattern for Africa is substantially different,

however. The youth population in Africa, which is already more than four times its 1950 level, is

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projected to continue growing until beyond 2050, with the 2050 level being eight times the level

of 1950.

Classifying demographic patterns

These trends in the size of the youth population are driven by complex changes in fertility,

mortality, and population momentum that will be discussed in detail below. Because these

underlying demographic trends have followed similar patterns in many countries, it is possible to

characterize four major patterns describing trends in the size of the youth population. The

criterion used to classify countries is the timing of the peak in the youth population. As

illustrated by the example of Latin America in Figure 1, this peak is often not a sharp peak, but

may be more characterized by a slow leveling off. For this reason the classification is based on

both the year in which the population aged 12-24 reaches a peak and the rates of growth of the

youth population around specific years. The definitions of the four specific categories are given

in Table 1.

Table 1. Definition of categories of the timing of the peak in the size of the population aged 12-24

Category Definition 1 Countries with peak in youth population before 2000

Definition: countries in which the maximum youth population over the period 1950-2050 occurred prior to 2000

2 Countries with peak between 2000 and 2010. Definition: countries in which the peak youth population occurs between 2000 and 2010 OR the peak population occurs after 2010 but the growth rate between 2005 and 2015 is below 0.5% per year

3 Countries with peak between 2010 and 2030 Definition: countries in which the peak youth population occurs between 2010 and 2030 and the growth rate between 2005 and 2015 is above 0.5% per year OR the peak youth population occurs after 2030 but the growth rate between 2025 and 2035 is below 0.5% per year

4 Countries with peak after 2030 Definition: countries with peak after 2030 and with growth rates above 0.5% per year between 2025 and 2035.

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Table 2 shows the distribution of the 154 countries included in our analysis into these four

classifications. While the largest number of countries fall in Group 4, it is important to keep in

mind that this does not necessarily represent a larger population than the other groups. Countries

with large populations in Group 2 include Indonesia, Brazil, Mexico, and Vietnam, the third,

fourth, eighth and ninth largest developing countries, respectively, in 2005. Group 3 includes

India, Bangladesh, and the Philippines, the second, sixth, and tenth largest developing countries.

Group 4 includes Pakistan and Nigeria, the fifth and seventh largest developing countries. With

China in Group 1, the population of developing countries is spread quite broadly across the four

classifications.

Table 2. Classification of countries by timing of peak in the population aged 12-24

Category Frequency Percent 1. Peak before 2000 31 20.1 2. Peak between 2000 and 2010 37 24.0 3. Peak between 2010 and 2030 31 20.1 4. Peak after 2030 55 35.7 Total 154 100

Appendix B provides a complete classification of 154 countries. Appendix C presents

figures showing the population aged 12-24 between 1950 and 2030 for each of these countries.

As would be expected from Figure 1, Asian and Latin American countries are mostly found in

Groups 1 and 2, while African countries dominate Groups 3 and 4.

Figures 2-5 show selected countries in each of the four groups. Figure 2 shows the two

major developing countries in Group 1, China and Thailand, countries, which had already

reached a peak in the size of the youth population before 2000. Both countries had rapid fertility

declines in the 1970s and 1980s, with Total Fertility Rates that were close to the replacement

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fertility level of 2.1 by the 1990s. The peak of the youth population was about 2.8 times its 1950

level in Thailand, about 2.4 times its 1950 level in China.

Figure 3 shows a group of countries in the second group, with a peak in the youth population

occurring roughly between 2000 and 2010 – Brazil, Costa Rica, Indonesia, Mexico, and Vietnam.

The magnitude of the peak, measured relative to the 1950 youth population, varies considerably.

Indonesia’s youth population will peak at around 2.5 times its 1950 level, Costa Rica’s youth

population will reach over 5 times its 1950 level, with the other countries in between. Note that

the peak is not very pronounced in any of these countries, with more of a long leveling off than a

sharp peak. Most of these countries are projected to have relatively constant youth populations

for several decades.

Figure 4 shows a group of countries that will reach a peak in their youth populations between

2010 and 2030 – India, Egypt, Malaysia, Peru and the Philippines. These are countries that have

had later fertility declines than the group of countries shown in Figures 2 and 3. As in Figure 2,

most of these countries are projected to experience 20 or 30 years of relatively constant youth

populations after they reach their peak.

Figure 5 shows a group of countries that still have rising youth populations in 2030 – the

Democratic Republic of Congo, Iraq, Kenya, Pakistan, and Senegal. As the figure demonstrates,

there is considerable heterogeneity within this group. Some countries with very late fertility

declines, such as Kenya and the Democratic Republic of Congo, are projected to have continued

rapid growth of the youth population for the next several decades. Other countries, such as

Pakistan and Senegal, are projected to have slower growth, leveling off shortly after 2030.

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The Impact of HIV/AIDS

For some countries, especially countries in southern Africa, assumptions about AIDS

mortality can have a significant impact on the projected size of the youth population. The

population projections of the U.N. Population Division include projections about AIDS mortality

and about the potential impact of anti-retroviral treatment. The population projections

incorporate the UNAIDS model that is used to project the course of the epidemic in the 60

countries with high HIV prevalence (UNAIDS Reference Group on Estimates, Modelling and

Projections, 2002). The complete set of projections also includes an alternative projection in

which it is assumed that there is no AIDS mortality. These alternative “No HIV/AIDS”

projections are made for the 60 countries with high HIV prevalence, with projections made from

1980 to 2050. Figure 6 shows the medium variant projection, which includes AIDS mortality,

and the alternative “No AIDS” projection for three countries with high HIV prevalence –

Botswana, South Africa, and Zambia. These projections use the 10-24 age group, rather than the

12-24 age group, since single year of age estimates are not available for the “no AIDS”

projections.

Figure 6 demonstrates that AIDS mortality has a substantial impact on the size of the youth

population in these southern African countries. There is little difference in the two projections in

2000, but by 2010 the projections begin to diverge. In Botswana, for example, the youth

population is projected to peak around 2005 by the medium variant projection, but under the

alternative “No AIDS” projection it would have continued increasing for several more decades.

The medium variant youth population in 2015 in Zambia is projected to be about 15% lower than

it would have been in the absence of HIV/AIDS.

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4. Why current youth cohorts are so large

It is useful to consider the demographic explanations for the large size of current youth

cohorts, and the explanations of why many countries are close to their peak youth population.

The explanations for these trends are important for a number of reasons. In addition to helping

understand the sources of the current “youth bulge,” an understanding of the underlying

processes helps put this bulge in perspective. It also helps us understand some important features

of today’s youth cohorts, such as the paradoxical fact that they were born into much smaller

families than those of their parents.

Key Features of the Demographic Transition

Demographers use the term “demographic transition” to refer to the pattern of changes in

fertility, mortality, and population growth that have been observed with a high degree of

regularity around the world. The stylized description of the demographic transition starts with a

regime in which both death rates and birth rates are relatively high and roughly equal, implying

low rates of population growth. The demographic transition begins with a decline in death rates,

potentially driven by a variety of factors. With death rates falling, birth rates typically remain

high for some period of time, generating population growth. Eventually birth rates also fall,

slowing the rate of population growth. The transition ends when birth rates and death rates have

both stabilized at a new low level, implying a return to low (or zero) population growth. The

pace at which mortality and fertility decline and the length of time between mortality decline and

fertility decline determine the rate of population growth that will be observed during the

demographic transition.

High-income countries, which went through the demographic transition in the 1800s or early

1900s, typically experienced long and relatively slow mortality decline. The gap between birth

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rates and death rates was never very large, and population growth rates rarely exceed 1% per

year. The demographic transition that took place in developing countries was quantitatively quite

different. Death rates declined very fast, generating population growth rates that exceeded 4%

per year in some countries (implying doubling times of less than 20 years).

It is instructive to look at the history of the demographic transition for a specific country.

This section will look at the case of Brazil, a country whose mortality decline and fertility decline

were fairly typical of countries that have now progressed quite far through the demographic

transition. Brazil has exceptionally good data with which to track demographic changes at both

the macro and micro level, including detailed census data back to 1960. Figure 7 shows the brod

overview of the demographic transition in Brazil, based on the United Nations estimates of birth

rates, death rates and population growth rates from 1950-55 to 2000-05.1 The figure shows that

the demographic transition was already well underway in Brazil by 1950, with the crude death

rate having fallen to 15 per 1000, while the crude birth rate was almost 45 per 1000. The rate of

population growth was about 28 per 1000, or 2.8% per year, in the 1950s, a rate much higher

than was ever experienced by currently high income countries when they went through the

demographic transition. Although the crude birth rate was falling in the 1950s, death rates were

falling faster, causing a peak population growth rate of 3.0% in the 1960-65 period. This was

also the period in which world population growth rates reached their historic peak, at around 2%

per year (Lam, 2005).

1 The crude birth rate is defined as the number of births per year divided by the total mid-year population, expressed as a ratio of births per 1000 population. The crude death rate is deaths per year per 1000 population. The rate of natural increase is simply the difference between the crude birth rate and the crude death rate. The rates shown are estimates of the average annual rates in each five-year period.

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It is important to look back at the rapid population growth of the 1960s, because this is in

many ways the origin of today’s large youth cohorts. If we consider the children born in

developing countries in the 1960s to be the children of the population explosion, today’s youth

cohorts are the grandchildren of the population explosion, the children of those earlier large

cohorts. It is important to keep in mind this fundamental aspect of today’s youth cohorts.

While the crude birth rates and crude death rates in Figure 7 are the ultimate determinants of

the population growth rate, they are not the best indicators of fertility behavior of basic mortality

conditions. Because they are simply ratios of the numbers of births and deaths to the total

population, they can be very sensitive to the age structure of the population. Figure 8 shows the

Total Fertility Rate (TFR) and Infant Mortality Rate in Brazil over the 1950-2005 period. The

TFR is the sum of age-specific fertility rates in a given year, and can be interpreted as the number

of births a woman would have in her lifetime, given the age-specific probabilities of birth in that

year. Unlike the crude birth rate, which is affected by the population age structure, the TFR is a

useful summary of the actual fertility behavior of women in a given period. The crude death rate

is also highly affected by the population age structure, and is thus a very imperfect measure of

the actual mortality situation in a given period. The infant mortality rate, which measures the

number of births that die before age 1 divided by the total number of births (expressed per 1000

births), is much better than the crude death rate at showing the decline in mortality. It was

declines in infant mortality that played the most important role in driving declines in the overall

death rate during the demographic transition. As shown in Figure 8, infant mortality was falling

rapidly in Brazil in the 1950s, but fertility did not begin to fall until the 1960s. These patterns for

Brazil are very typical of patterns in fertility and mortality throughout the developing world

between 1950 and 2000. While the timing and pace of the decline varies across regions, virtually

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all developing countries experienced rapid mortality decline, a period of high population growth,

and eventually fertility decline.

The size of surviving birth cohorts during the demographic transition was influenced by a

complex interaction of fertility, mortality, and population momentum. Population momentum

refers to the inertia that is inherent in population dynamics due to the fact that childbearing takes

place two to three decades after birth. The rapid growth of birth cohorts in the 1950s and 1960s

produced a rapid growth of the childbearing-age population in the 1970s, 1980s, and beyond.

This growth of the childbearing-age population competed with the decline in fertility rates to

determine the size of birth cohorts.

A good picture of trends in cohort size is provided by looking again at the case of Brazil,

where excellent census data back to 1960 provides an unusually detailed record. Figure 9 shows

the size of cohorts as they appear in the censuses from 1960 to 2000, using only the age groups

below the age of 20 in each census. These are not the size of the birth cohorts at the time they

were born, but rather are the size of the birth cohort as it had survived to the census. It is thus a

fairly good measure of the size of surviving birth cohorts, a useful measure if we are interested in

the size of youth cohorts. The figure shows that birth cohorts grew rapidly in the 1950s and

1960s, leveled off in the late 1960s and early 1970s, then grew rapidly again in the late 1970s,

reaching a peak in 1982. These trends in cohort size provide a convenient summary of the

population dynamics of the demographic transition. The rapid growth in the size of surviving

birth cohorts during the 1950s and early 1960s reflected the rapid decline of infant and child

mortality. The leveling off in the late 1960s reflected the rapid decline in fertility rates that

began in the 1960s in Brazil. The rapid growth in the size of cohorts born in the late 1970s was

due to population momentum, resulting from the increase in the size of the childbearing

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population as the birth cohorts of the 1950s reached childbearing age. The largest birth cohort in

Brazil was born in 1982, the point at which falling fertility finally overtook population

momentum. The experience for other countries will have been similar to that for Brazil, with

differences only in the specific timing of the largest cohort.

5. Trends in Growth Rates and Relative Cohort Size

While there are many reasons to take note of the unprecedented size of current youth

cohorts, for many economic issues it may be the growth rate of the youth population, or its size

relative to other age groups, that is the critical variable. As suggested in Figures 1-5, the growth

rates of youth populations in most countries are lower today than they were in previous decades.

Indeed, it is virtually a mathematical necessity that the growth rates must be declining if the total

size of the youth population is near its peak, given the smoothness inherent in most population

dynamics. Figure 10 shows the annual growth rate of the youth population in the same set of

countries shown in Figures 2-5 above (note that the vertical scales differ across panels). The

growth rate shown is the average annual rate of growth between two of the five-year population

totals shown in Figures 2-5 (for example, the first point in each graph is placed at 1952.5, and

represents the average annual growth rate between 1950 and 1955). Looking across all four

panels of Figure 10, several important features can be seen. Most importantly, the peak in the

growth rate of the youth population occurs around the 1965-70 period in almost all of the

countries, especially for countries in the first three classifications.

It is interesting to compare, for example, Thailand in Panel A and the Philippines in Panel C.

Thailand, which experienced its largest youth population around 1990, had its highest rate of

growth of the youth population in 1965-70, a growth rate of about 4%. The youth population in

the Philippines is projected to continue growing until around 2020, with a growth rate in the

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2000-2010 period of about 1.5% per year. But the peak growth rate of the youth population in

the Philippines also occurred in the 1965-70 period, with a growth rate of 3.7%. It is noteworthy

how many countries shown in Figure 10 had relatively similar experiences, reaching a peak

growth rate of the youth population around 1970 of about 3-4% per year. The major differences

across countries in the first three panels of Figure 10 are less in the timing and magnitude of the

peak growth rate than in the speed at which the growth rate declined in the 1980s and 1990s.

This reflects the fact that most developing countries experienced similar levels of rapid

population growth in the 1960s. The subsequent rate of decline in fertility rates differed

substantially across countries, however, and it these differences that drive the differences in the

rate of decline in the growth rate of the youth population during the 1980s and 1990s.

For some economic and social questions it may be the percentage of the population in youth

age groups that is most important. Figure 11 shows the percentage of the total population that is

aged 12-24, continuing to use the same countries and population projections used thus far. An

important lesson of Figure 11 is that most countries have already experienced the peak in the

percentage of the population that is aged 12-24. This peak occurs at around 25-30% of the

population in most countries.

While the youth population as a percentage of the total population may be important for

some purposes, a more important indicator may be the size of the youth population relative to the

working age population. As will be discussed below, two important strands of literature in

economic demography have focused on relative cohort size – the literature on the impact of

cohort size on the youth labor market, and the literature on the “window of opportunity” resulting

from a favorable ratio of the working age population to the dependent population. Figure 12

shows the ratio of the population aged 12-24 to the population aged 25-59. Once again we see

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that the period of the most dramatic relative numbers of youth were experienced several decades

ago in most countries. Most countries in the first three panels of Figure 12 experienced the

largest peak of the youth population relative to the working age population in the 1970s or 1980s.

As was the case with the growth rate of the youth population, in many cases we see similar

patterns in the timing and magnitude of the peak in the relative size of youth cohorts for countries

that look quite different in terms of the timing of the peak in the total size of the youth

population. Thailand and the Philippines both reached a peak around 1975, when there were

about 0.9 12-24 year-olds for every 25-59 year-old. The difference in the countries is that this

ratio had fallen to about 0.5 in Thailand in 2005, while in the Philippines it was still over 0.7.

Even in the countries in Panel D, where the youth population will continue to grow for the

next several decades, the ratio of the youth population to the working-age population has in most

cases already begun to decline. To the extent that this ratio is a critical measure of the pressure

the youth population places on resources or labor markets, it is important to recognize that the

pressure has already begun to diminish in most countries.

6. Family Size versus Cohort Size

One of the important, if somewhat paradoxical, features of population dynamics during the

demographic transition is that the size of birth cohorts can move in the opposite direction from

the size of families. This is another consequence of population momentum, which can cause a

gap in the timing between the decline in fertility and the decline in the size of birth cohorts. Lam

and Marteleto (2005) analyze the case of Brazil, using the detailed census and survey data back

to 1960. As can be seen by comparing Figure 8 and Figure 9 above, there is roughly a fifteen

year delay in Brazil between the onset of fertility decline, which occurs in the late 1960s, and the

birth of the largest birth cohort in 1982. This means that there is a period in which family size is

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on the decrease while cohort size is on the increase, potentially creating offsetting effects in

terms of resources available to children and youth.

Using the micro data from the Brazilian censuses of 1960, 1970, 1980, 1991, and 2000, Lam

and Marteleto analyze the number of siblings born to different cohorts of young people. These

estimates are made by matching young people to their mothers in the census and using the

mothers’ reports of children ever born and children surviving. Figure 13 shows the average

number of siblings ever born and siblings still surviving at the time of the census for youth aged

12-14 in each of these Brazilian censuses. Note that the surviving number of siblings increases

slightly between 1960 and 1970, even though the number of siblings ever born declines. This

represents the effect of rapidly falling infant and child mortality, which more than offset the

decline in fertility that had already begun to appear. Between the 1970 and 1980 censuses the

impact of falling fertility more than offset the impact of declines in infant and child mortality,

with young people in 1980 having fewer surviving siblings than their counterparts in 1970. The

number of surviving siblings fell by almost one child between the 1980 and 1991 censuses and

again between the 1991 and 2000 censuses. The cumulative decline between 1960 and 2000 was

2.3 siblings, a 44% decline. As shown by Lam and Marteleto, this decline was driven in

particular by a decline in the prevalence of large families. The result was a significant decline in

the competition for resources within families, potentially an important factor affecting their

health and schooling. At the same time, most of these young people were being born at a time

when cohort size was rapidly increasing, implying increased competition for resources at the

macro level. The pattern observed in Brazil is similar to that in most developing countries.

Many of today’s young people grew up in a period in which cohort size was increasing but

family size was decreasing, implying that they have had to compete with rising numbers of

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children from other families in getting access to school or health care, at the same time that they

have had to compete with fewer siblings in their own families.

7. Comparisons to Youth Demography in High-income Countries

Before discussing the economic implications of trends in youth demography in low-income

and middle-income countries, it is interesting to compare these trends to those observed in recent

decades in high-income countries. A large literature focused on the impact on the labor market

of the baby boom cohort in the United States and a related “youth bulge” in Europe. How did the

changes in the relative size of the youth population in the United Sates and Europe compare to

the changes that have been taking place in developing regions?

Figure 14 shows the growth rate of the youth population and the ratio of the youth

population to the working-age population for the France, Netherlands, United Kingdom, and

United States, countries in which there has considerable focus on the implications of youth cohort

size in recent decades. Looking at the annual growth rates in Panel A, it is interesting to note that

growth rates approaching 4% per year were seen in several countries. These are similar to the

peak growth rates of the youth population observed in most developing countries, as shown in

Figure 10. The cause of these spikes in the youth population was very different in the high-

income countries than it was in the developing countries. The fluctuations in age structure in the

high-income countries were driven by sudden increases in the birth rate in the 1950s. These baby

booms were followed by equally rapid declines in birth rates, generating large swings in the size

of cohorts born over these decades.

Looking at the ratio of the youth population to the working-age population in Panel B, these

levels also exhibit a range of variation over short periods of time that is similar to the range

experienced in developing countries. The absolute level of this ratio remains at levels well below

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those observed in developing countries during the 1970s and 1980s, however. In the United

States the ratio of the youth population to the working-age population fell by 38% between the

1975 and 2000, from 0.6 to 0.37. Thailand and Brazil also had a peak in this ratio in 1975, with

declines between 1975 and 2000 of 44% in Thailand and 27% in Brazil. An important difference

is that Thailand and Brazil, like most of the other countries shown in Figure 12, had continued

declines in the relative size of the youth population after 2000, while the United States and

Europe have had a relatively constant ratio of the youth population to the working-age

population.

8. Economic Implications of Youth Demography

The demographic patterns documented above have potentially important economic

implications. The following sections discuss specific areas in which youth demography may

make a significant economic impact, with particular focus on labor markets, savings, and public

expenditures.

Youth Labor Markets

One of the most obvious areas in which to consider the economic implications of changes in

the absolute and relative numbers of young people is in the youth labor market. Economic theory

predicts that the absolute size, relative size, and rate of growth of the youth population could all

potentially have an impact on the wages and employment opportunities of young people. Which

of these variables will have the largest impact will depend on the nature of the labor market, the

degree of complementarity or substitutability of youth labor with other factors of production, and

the speed of adjustment of other factors in response to changes in the supply of youth in the labor

market. The absolute size of the youth population will be important in relationship to the total

quantity of other factors of production. Some of these factors, such as land, may in fact be

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relatively fixed in supply, though in practice the supply of land has been a relatively unimportant

component in explaining economic growth, wage levels, or levels of poverty even during the last

fifty years of rapid population growth (Johnson, 2000, Lam, 2005).

Physical capital is the most important non-labor factor or production. While it is beyond the

scope of this paper to discuss the determinants of capital accumulation, there is little reason to

think that the capital stock is independent of the absolute size or rate of growth of the labor force.

This implies that the absolute size of the youth population is unlikely to have any particular

economic significance in and of itself, especially in terms of the labor market. A high rate of

growth of the youth population, however, may be of more economic interest, since it may push

against the rate of growth of physical capital, potentially leading to lower wages or at least lower

wage growth. This will be discussed below in regard to the impact of cohort size on savings. To

the extent that it is the growth rate of the youth labor market that has important economic

implications, it is important to recall from Figure 10 that the rate of growth of the youth

population has been declining in most developing countries for several decades. While a number

of countries, especially in Africa and south Asia, continue to have growth rates of their youth

population of around 2% per year, a rate that can add significantly to the employment challenges

facing these countries, these growth rates are comfortably below the 3% or 4% growth rates that

were experienced by most developing countries in the 1970s, and the growth rates are steadily

declining.

In addition to land and physical capital, youth labor gets combined with the labor of more

experienced workers. The relationship between youth workers and older workers may be one of

the most important economic aspects of youth cohort size, and has been the focus of attention in

several strands of literature. As mentioned above, there is a large literature in labor economics

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and economic demography analyzing the impact of cohort size on the labor market in the United

States and Europe. Research on the United States, such as Freeman (1979), Welch (1979), and

Berger (1985, 1989), looked at the initial earnings and subsequent earning growth of the large

baby boom cohort that entered the labor market in the 1970s. Many European countries also

experienced declining fertility in the 1960s that resulted in declines in the size of youth cohorts

entering the labor market in the 1980s. Korenman and Neumark (2000) provide a useful review

of the research that analyzing the impact of cohort size on labor markets in Europe.

A broad consensus from the research on the labor market impact of relative cohort size in the

United States and Europe is that there does appear to be a negative impact of relative youth

cohort size on the wages and employment opportunities of young people. Most of these studies

use some measure of the relative number of young workers to older workers, similar to the

relative cohort size used in Figure 12. Most studies that examine the longer term experience of

cohorts find that the negative impact of being in a large youth cohort tends to diminish over time.

In some studies, the disadvantage of being in a large cohort almost completely disappears as the

cohort ages. Other studies find more persistent effects. Using cross-national data from OECD

countries from 1970 to 1994, Korenman and Neumark estimate an elasticity of youth

unemployment to relative cohort size of around 0.5. They conclude that the impact of relative

cohort size is easily swamped by other macroeconomic and labor market conditions, and argue

that cohort size per se is unlikely to have a major impact on unemployment rates in OECD

countries.

While it is potentially misleading to apply the results of this research on OECD countries to

developing countries, several points are worth noting. First, as shown in the figures above, the

fluctuations in relative cohort size in OECD countries are roughly similar in magnitude to the

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changes experienced by developing countries. It is therefore perhaps reasonable to draw

inferences based on the OECD experience, with the literature suggesting that larger relative

cohort size will have a negative but relatively modest impact on youth employment prospects. A

second point is that the relative size of youth cohorts, the measure that is the focus of study in

most of this research, peaked in the 1970s and 1980s in many developing countries, and is

beginning to decline even in the countries with the latest fertility decline. Combining the

empirical evidence on the impact of cohort size with the demographic patterns, there would seem

to be little reason to expect that developing countries will be experiencing a youth labor market

crisis in the coming decades, at least as far as demographic pressure alone is concerned. On the

contrary, the period of most rapid pressure on youth wages and employment has already been

passed in most countries.

Relative Cohort Size, Savings, and Public Expenditures

Another important area in which there has been attention to the economic implications of

relative cohort size is the literature on dependency burden and savings rates. This literature has a

long history, going back to the work of Coale and Hoover (1958) and Leff (1969), who argued

that countries with high population growth rates suffered from low savings rates due to the high

ratio of children and youth to the working-age population. These arguments have taken on a

somewhat different form in a body of work that focuses on the rapid changes in dependency

burdens that take place during the demographic transition. Bloom and Williamson (1998) argue

that a rapid rise in the ratio of the working population to the non-working population in East Asia

between 1965 and 1990 played an important role in driving the East Asian “economic miracle.”

Bloom and Williamson argue that part of the effect of having faster growth in the working-age

population is due simply to the faster growth of the labor force, increasing per capita output due

21

to a concentration of the population in productive ages. In addition to this, however, they argue

that there is an effect that works through savings and capital accumulation. This effect, which

recalls the arguments of Coale and Hoover, involves a negative impact of the dependency rate on

savings. The impact of dependency rates on aggregate savings derives in part from life cycle

savings patterns, with older adults saving more than young adults.

The idea that countries experience a “demographic dividend” or “demographic bonus” when

the working age population begins to grow faster than the pre-working-age population has been

pursued in papers such as Bloom, Canning, and Malaney (2000) and Bloom and Canning (2001).

While work such as Deaton and Paxson (1997) find limited support in micro-level data for the

hypothesized link between age structure, savings, and economic growth, the idea of a

demographic bonus has intuitive appeal in a number of dimensions. Some of these effects may

be particularly relevant for youth. As pointed out by Lee (1994), public expenditures, especially

when they are funded by taxes on income or consumption (as opposed to, say, mineral wealth)

will be affected by the ratio of the taxpaying population to the beneficiary population. Youth will

typically be net beneficiaries rather than net taxpayers, depending on the state to finance primary

and secondary schooling, post-secondary education, training programs, and health programs.

The relevant demographic variable will be the ratio of the youth population to the working-age

population, although the growth rate of the youth population may also play a role to the extent

that public expenditures are slow to respond to changing demand (as in the case of schools, for

example, where physical facilities and the training of teachers will create inertia, even aside from

funding issues).

As emphasized above, although the youth population is the largest it has ever been in most

developing countries, the size of the youth population relative to the working-age population is

22

lower in most countries than it was 20 years ago. The declines in this ratio have been

considerable, with declines of 25% to 50% in many countries between their historic peak and the

levels observed in 2005. Taken literally, a 25% decline in this ratio, a decline that is quite

common across developing countries, implies that a given dollar collected in taxes for secondary

schooling from each working-age person could pay for a 33% increase in school pending per

youth in 2005, compared to the year of the peak ratio. These ratios are on the decline in almost

every developing country, implying that from this perspective the economic circumstances for

investing in youth are steadily improving.

Conclusions

This paper has documented the demography of youth in developing countries. Using the

medium variant projections of the United Nations Population Division, it is clear that the youth

population – the population aged 12-24 – in most developing countries is the largest it has ever

been. In a number of countries the youth population is the largest it will be for many decades,

and plausibly the largest it will ever be. Four broad patterns describe the timing of the peak in

the youth population. A few countries, notably Thailand and China, already experienced the

peak in their youth population before 2000, and are on a clear downward trajectory. A large

second group, including numerous Latin American countries and East Asian countries, is

experiencing a peak between 2000 and 2010. A third group, characterized by somewhat slower

fertility decline than the countries in the first two groups, is projected to reach a peak youth

population between 2010 and 2030. A fourth group of countries, including most of Africa as

well as some South Asian countries, is projected to continue with growth of its youth population

until beyond 2030. These countries tend to be the poorest countries in the world, and have been

late to experience declines in fertility.

23

As illustrated with the specific case of Brazil, the explanation for today’s large youth cohorts

and for the timing of the peak in youth populations is directly linked to the rapid changes in

mortality and fertility that produced the demographic transition. Today’s youth cohorts are

broadly speaking the children of the large cohorts born during the population explosion that

peaked in the 1960s. Paradoxically, they were born during a period of rapidly falling fertility,

with the large size of their cohorts driven by population momentum rather than high fertility.

Because of this, today’s youth were typically born into much smaller families were their parents.

They have thus grown up with unusually heavy competition for resources due to large cohort size

at the macro level, while competing with smaller numbers of siblings at the micro level.

The population dynamics of the demographic transition lead to a number of important

features of today’s youth demography. Although the absolute size of the cohorts is historically

unprecedented, the growth rate of the youth population reached a peak in most developing

countries around 1970. Growth rates of the youth population are well below the peak growth

rates of the 1960s and 1970s, and continue to decline in most developing countries. This is true

even in the group of poor countries the slowest fertility decline. Similar patterns are observed for

the percentage of the population aged 12-24 and for the ratio of the youth population to the

working-age population. The youth percentage reached a peak in many countries before 2000,

although it will continue to rise in some of the countries that have the latest peak in the total

youth population. The ratio of the youth population to the working-age population peaked

considerably sooner than the youth percentage, having peaked in most countries in the 1970s or

1980s. This ratio is at or near its peak even in most of the countries with the highest fertility.

There are potentially important economic implications from these trends in youth

demography. The absolute size, the rate of growth, and the relative size of the youth population

24

could all have an impact on economic growth rates, wages, unemployment, and public

expenditures. Economic theory suggests that absolute size per se is probably the least important

economic variable, however, since fixed factors of production are unlikely to be significant in

these economies. The rate of growth of the youth population and the size of the youth population

relative to other age groups are more likely to have important economic effects. A large

literature on the impact of relative cohort size on labor markets in the United States and Europe

suggests that large youth cohorts relative to the working-age population could have moderate

negative effects on wages and employment. Research on the impact of age structure on

economic growth in developing countries suggests that a large youth population relative to the

working-age population can also have negative effects on savings and economic growth.

While this evidence suggests that large youth populations may have potentially negative

economic effects, it is important that most developing countries have already experienced the

peak in their youth population relative to the working-age population. This ratio has declined

considerably since its peak in most countries, and will continue to decline in the coming decades.

Countries that will have continued growth of the youth population beyond 2030 have the highest

ratios of youth population to the working-age population, and many are just beginning to see the

ratio decline. These countries face substantial challenges in providing youth employment and

providing health and education services to these large youth cohorts. The ratio of youth to the

working-age population is projected to decline in most of these countries over the coming

decades, however, providing some relief as they try to meet the needs of their youth populations.

25

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27

Figure 1. Population Aged 12-24, World and Major Developing Regions (1950=100)

0

100

200

300

400

500

600

700

800

900

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

Year

Popu

latio

n (1

950=

100)

AfricaLatin America and the CaribbeanAsiaWorld

28

Figure 2. Selected Countries with Peak in Youth Population before 2000Population Aged 12-24 (1950=100), U.N. Medium Variant Projections

0

50

100

150

200

250

300

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

Year

Popu

latio

n (1

950=

100)

ThailandChina

29

Figure 3. Selected Countries with Peak in Youth Population in 2000-2010 Population Aged 12-24 (1950=100), U.N. Medium Variant Projections

0

100

200

300

400

500

600

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

Year

Popu

latio

n (1

950=

100)

Costa RicaMexicoBrazilViet NamIndonesia

30

Figure 4. Selected Countries with Peak in Youth Population in 2010-2030 Population Aged 12-24 (1950=100), U.N. Medium Variant Projections

0

100

200

300

400

500

600

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

Year

Popu

latio

n (1

950=

100)

PhilippinesMalaysiaEgyptPeruIndia

31

Figure 5. Selected Countries with Peak in Youth Population after 2030 Population Aged 12-24 (1950=100), U.N. Medium Variant Projections

0

200

400

600

800

1000

1200

1400

1600

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

Year

Popu

latio

n (1

950=

100)

Kenya

Democratic Republic of the Congo

Iraq

Senegal

Pakistan

32

Figure 6. Projected Youth Population with and without AIDSPopulation Aged 10-24 (1980=100), U.N. Medium Variant Projection and "No AIDS" Projection

0

50

100

150

200

250

300

350

400

450

500

1980 1985 1990 1995 2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

Year

Popu

latio

n (1

980=

100)

Zambia - No AIDSZambia - AIDSBotswana - No AIDSBotswana - AIDSSouth Africa - No AIDSSouth Africa - AIDS

33

Figure 7. Demographic Transition in BrazilCrude Birth Rate, Crude Death Rate, and Rate of Natural Increase, 1950-2005

0

5

10

15

20

25

30

35

40

45

50

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

Year

Rat

e pe

r 100

0 po

pula

tion

Crude birth rate

Rate of natural increase

Crude death rate

34

Figure 8. Total Fertility Rate and Infant Mortality Rate, Brazil 1950-2005

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005

Year

Tota

l Fer

tility

Rat

e (b

irth

s pe

r w

oman

)

0

20

40

60

80

100

120

140

160

Infa

nt M

orta

lity

Rat

e (p

er 1

000

birt

hs)

Total fertility rate

Infant mortality rate

35

Figure 9. Cohort Size in the Brazilian Censuses from 1960 to 2000

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

4.0

1940 1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000Year of Birth

Num

ber R

epor

ted

in C

ensu

s (m

illio

ns)

2000 census1991 census1980 census1970 census1960 census

36

Figure 10. Growth Rate of Population Aged 9-12, 1950-2050, United Nations Medium Variant Projections

Figure 10a. Selected Countries with Peak in Youth Population before 2000Growth Rate of Population Aged 12-24, U.N. Medium Variant Projections

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

Year

Ann

ual G

row

th R

ate

Thailand

China

Figure 10b. Selected Countries with Peak in Youth Population in 2000-2010 Growth Rate of Population Aged 12-24, U.N. Medium Variant Projections

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

Year

Ann

ual G

row

th R

ate

Costa RicaMexicoBrazilViet NamIndonesia

Figure 10c. Selected Countries with Peak in Youth Population in 2010-2030 Growth Rate of Population Aged 12-24, U.N. Medium Variant Projections

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

Year

Ann

ual G

row

th R

ate

PhilippinesMalaysiaEgyptPeruIndia

Figure 10d. Selected Countries with Peak in Youth Population after 2030 Growth Rate of Population Aged 12-24, U.N. Medium Variant Projections

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

6.0%

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

Year

Ann

ual G

row

th R

ate

KenyaDemocratic Republic of the CongoIraqSenegalPakistan

37

Figure 11. Percentage of Population Aged 12-24, 1950-2050, United Nations Medium Variant Projections

Figure 10a. Selected Countries with Peak in Youth Population before 2000Percentage of Population Aged 12-24, U.N. Medium Variant Projections

0

5

10

15

20

25

30

35

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

Year

Perc

enta

ge

Thailand

China

Figure 10b. Selected Countries with Peak in Youth Population in 2000-2010 Percentage of Population Aged 12-24, U.N. Medium Variant Projections

0

5

10

15

20

25

30

35

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

Year

Perc

enta

ge

Costa RicaMexicoBrazilViet NamIndonesia

Figure 10c. Selected Countries with Peak in Youth Population in 2010-2030 Percentage of Population Aged 12-24, U.N. Medium Variant Projections

0

5

10

15

20

25

30

35

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

Year

Perc

enta

ge

PhilippinesMalaysiaEgyptPeruIndia

Figure 10d. Selected Countries with Peak in Youth Population after 2030 Percentage of Population Aged 12-24, U.N. Medium Variant Projections

0

5

10

15

20

25

30

35

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

Year

Perc

enta

ge

KenyaDemocratic Republic of the CongoIraqSenegalPakistan

38

Figure 12. Ratio of Population Aged 12-24 to Population Aged 25-59, 1950-2050, United Nations Medium Variant Projections

Figure 11a. Selected Countries with Peak in Youth Population before 2000Population Aged 12-24 over Population Aged 25-59, U.N. Medium Variant Projections

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

Year

Rat

io

Thailand

China

Figure 11b. Selected Countries with Peak in Youth Population in 2000-2010 Population Aged 12-24 over Population Aged 25-59, U.N. Medium Variant Projections

0

0.2

0.4

0.6

0.8

1

1.2

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

Year

Rat

io

Costa RicaMexicoBrazilViet NamIndonesia

Figure 11c. Selected Countries with Peak in Youth Population in 2010-2030 Population Aged 12-24 over Population Aged 25-59, U.N. Medium Variant Projections

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

Year

Rat

io

PhilippinesMalaysiaEgyptPeruIndia

Figure 11d. Selected Countries with Peak in Youth Population after 2030 Population 12-24 over Population 25-59, U.N. Medium Variant Projections

-

0.2

0.4

0.6

0.8

1.0

1.2

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

Year

Rat

io

KenyaDemocratic Republic of the CongoIraqSenegalPakistan

39

Figure 13. Average Number of Siblings of Brazilian Youth Aged 12-14, Brazil 1960-2000 Census Data

0.0

1.0

2.0

3.0

4.0

5.0

6.0

7.0

8.0

1960 1970 1980 1990 2000

Num

ber

of s

iblin

gs

Siblings ever bornSiblings surving

40

Figure 14. Measures of Youth Demography for the France, Netherlands, United Kingdom, and United States, 1950-2050, United Nations Medium Variant Projections

Panel A. Growth Rate of Population Aged 12-24, U.N. Medium Variant Projections

-3.0%

-2.0%

-1.0%

0.0%

1.0%

2.0%

3.0%

4.0%

5.0%

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

Year

Ann

ual G

row

th R

ate

United States of America

France

Netherlands

United Kingdom

Panel B. Ratio of Population Aged 12-24 to Population Aged 25-59, U.N. Medium Variant Projections

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050

Year

Rat

io

United States of America

France

Netherlands

United Kingdom

41

The demography of youth in developing countries and its economic implications

David Lam

November 2005

Appendix

Appendix A. Assumptions used in the United Nations Population Projections

(taken from http://esa.un.org/unpp/index.asp?panel=4 on October 31, 2005)

ASSUMPTIONS UNDERLYING THE RESULTS OF THE 2004 REVISION OF WORLD

POPULATION PROSPECTS

The future population of each country is projected from an estimated population for 1 July 2005. Because

actual population data for 2005 are not yet available, the 2005 estimate is based upon the most recent population

data available for each country, derived usually from a census or population register, updated to 2005 using all

available data on fertility, mortality and international migration. In cases where very recent data are not

available, estimated demographic trends are short term projections from the most recent available data.

Population data from all sources are evaluated for completeness, accuracy and consistency, and adjusted where

necessary.

To project population until 2050, the United Nations Population Division applies assumptions regarding

future trends in fertility, mortality, and migration. Because future trends cannot be known with certainty, a

number of projection variants are produced.

The 2004 Revision includes six projection variants. The results for four are available on the web. These

four variants differ among themselves with respect to the assumptions made regarding the future course of

fertility.

To describe the different projection variants, the assumptions made regarding fertility, mortality and

international migration are described first.

A. Fertility assumptions: Convergence toward total fertility below replacement

42

Fertility assumptions are described in terms of the following groups of countries:

High-fertility countries: Countries that until 2005 had had no fertility reduction or only an incipient decline;

Medium-fertility countries: Countries where fertility has been declining but whose level was still above 2.1

children per woman in 2000-2005;

Low-fertility countries: Countries with total fertility at or below 2.1 children per woman in 2000-2005.

Medium-fertility assumptions:

Total fertility in all countries is assumed to converge eventually toward a level of 1.85 children per

woman. However, not all countries reach this level during the projection period, that is, by 2050. The basic

principle of fertility projection is the same for all countries, but projection procedures are slightly different

depending on whether countries had a total fertility above or below 1.85 children per woman in 2000-2005.

Fertility in high-fertility and medium-fertility countries is assumed to follow a path derived from models of

fertility decline established by the United Nations Population Division on the basis of the past experience of all

countries with declining fertility during 1950-2005. The models relate the level of total fertility during a period to

the average expected decline in total fertility during the next period. If the total fertility projected by a model for a

country falls to 1.85 children per woman before 2050, total fertility is held constant at that level for the remainder of

the projection period (that is, until 2050). That is, 1.85 children per woman represents a floor value below which the

total fertility of high and medium-fertility countries is not allowed to drop before 2050. However, it is not necessary

for all countries to reach the floor value by 2050. If the model of fertility change used produces a total fertility above

1.85 children per woman for 2045-2050, that value is used in projecting the population. In all cases, the projected

fertility paths yielded by the models are checked against recent trends in fertility for each country. When a country’s

recent fertility trends deviate considerably from those consistent with the models, fertility is projected over an initial

period of 5 or 10 years in such a way that it follows recent experience. The model projection takes over after that

transition period. For instance, in countries where fertility has stalled or where there is no evidence of fertility

decline, fertility is projected to remain constant for several more years before a declining path sets in.

43

Fertility in low-fertility countries is generally assumed to remain below 2.1 children per woman during most of

the projection period and reach 1.85 children per woman by 2045-2050. For countries where total fertility was below

1.85 children per woman in 2000-2005, it is assumed that over the first 5 or 10 years of the projection period fertility

will follow the recently observed trends in each country. After that transition period, fertility is assumed to increase

linearly at a rate of 0.07 children per woman per quinquennium. Thus, countries whose fertility is currently very low

need not reach a level of 1.85 children per woman by 2050.

High-fertility assumptions:

Under the high variant, fertility is projected to remain 0.5 children above the fertility in the medium variant

over most of the projection period. By 2045-2050, fertility in the high variant is therefore half a child higher than

that of the medium variant. That is, countries reaching a total fertility of 1.85 children per woman in the medium

variant have a total fertility of 2.35 children per woman in the high variant at the end of the projection period.

Low-fertility assumptions:

Under the low variant, fertility is projected to remain 0.5 children below the fertility in the medium variant over

most of the projection period. By 2045-2050, fertility in the low variant is therefore half a child lower than that of

the medium variant. That is, countries reaching a total fertility of 1.85 children per woman in the medium variant

have a total fertility of 1.35 children per woman in the low variant at the end of the projection period.

Constant-fertility assumption:

For each country, fertility remains constant at the level estimated for 2000-2005.

B. Mortality assumptions: Increasing life expectancy except when affected by HIV/AIDS

Normal-mortality assumption:

Mortality is projected on the basis of models of change of life expectancy produced by the United Nations

Population Division. These models produce smaller gains the higher the life expectancy already reached. The

selection of a model for each country is based on recent trends in life expectancy by sex. For countries highly

affected by the HIV/AIDS epidemic, the model incorporating a slow pace of mortality decline has generally been

used to project the reduction of general mortality risks not related to HIV/AIDS.

44

The impact of HIV/AIDS on mortality:

For the 60 countries highly affected by the HIV/AIDS epidemic, estimates of the impact of HIV/AIDS are

made by explicitly modelling the course of the epidemic and by projecting the yearly incidence of HIV infection.

The model developed by the UNAIDS Reference Group on Estimates, Modelling and Projections2 is used to fit past

estimates of HIV prevalence provided by UNAIDS so as to derive the parameters determining the past dynamics of

the epidemic. For most countries, the model is fitted assuming that the relevant parameters have remained constant

in the past. Beginning in 2005, the parameter PHI, which reflects the rate of recruitment of new individuals into the

high-risk or susceptible group, is projected to decline by half every thirty years. The parameter R, which represents

the force of infection, is projected to decline in the same manner. The reduction in R reflects the assumption that

changes in behaviour among those subject to the risk of infection, along with increases in access to treatment for

those infected, will reduce the chances of transmitting the virus. The rate of mother-to-child transmission is

projected to decline at varying rates, depending on each country’s progress in increasing access to treatment. In

addition, the component of the Reference Group model relative to the survivorship of infected children has been

updated: in the 2004 Revision it is assumed that 50 per cent of children infected through mother-to-child

transmission will survive to age two.

The 2004 Revision incorporates for the first time a longer survival for persons receiving treatment with highly

active antiretroviral therapy (ART). The proportion of the HIV-positive population receiving treatment in each

country is consistent with estimates prepared by the World Health Organization for end of 200432. Coverage is

projected to reach between 40 percent and 85 per cent by 2015, depending on the current level of coverage. It is

assumed that, on average, annual survival probabilities increase to at least 80 for individuals receiving ART. Under

this assumption, mean survival from the initiation of therapy is 3.1 years (median 4.5 years). In contrast, in the

absence of treatment mean survival after progression to AIDS is assumed to be just one year.

2 "Improved methods and assumptions for estimation of the HIV/AIDS epidemic and its impact: Recommendations

of the UNAIDS Reference Group on Estimates, Modelling and Projections". AIDS, vol. 16, pp. W1-W14 (UNAIDS Reference Group on Estimates, Modelling and Projections, 2002).

3 World Health Organization. “3 by 5” Progress Report, December 2004. WHO and UNAIDS.

45

C. International migration assumptions

Normal-migration assumption:

The future path of international migration is set on the basis of past international migration estimates and an

assessment of the policy stance of countries with regard to future international migration flows.

D. The projection variants

Table 1 presents in a schematic way the different assumptions underlying the four projection variants. As

shown, the four variants (low, medium, high and constant-fertility) share the same assumptions regarding mortality

and international migration. They differ among themselves only with respect to the assumptions regarding fertility.

A comparison of their results allows therefore an assessment of the effects that different fertility paths have on other

demographic parameters.

Table 1. Projection variants in terms of assumptions for fertility, mortality and international migration

Assumptions

Projection variant Fertility Mortality International

migration

Low Low Normal Normal

Medium Medium Normal Normal

High High Normal Normal

Constant-fertility Constant Normal Normal

Constant-mortality Medium Constant Normal

Zero-migration Medium Normal Zero

E. Methodological changes made for the 2004 Revision

In the medium variant, the fertility of countries with a total fertility below 1.85 children per woman in 2000-

2005 is projected first by continuing recent trends and then by increasing fertility linearly at a rate of 0.07 children

per woman per quinquennium. These countries do not necessarily reach a level of 1.85 children per woman by 2050.

46

In the 2004 Revision, additional models of mortality change have been used to capture the diversity of

historical experience in the rise of life expectancy. Specifically, very slow and very fast models of change have been

developed and added to the previously existing slow, medium and fast models.

The impact of HIV/AIDS on mortality is modelled explicitly for all countries that had adult HIV prevalence of one

per cent or greater in 2003.

Treatment with antiretroviral therapy is explicitly incorporated into the projection of HIV/AIDS for affected

countries. In addition, the rate of mother-to-child transmission of HIV is projected to decline at a rate consistent with

projected progress in expanding access to treatment.

Appendix B. Classification of countries by timing of peak in population aged

12-24

Average annual

growth

Classific

ation

U.N.

country

code Country

Year of

peak

2005-

2015

2025-

2035

1. Countries with peak before 2000

1 152 Albania 1989 -0.5% 0.1%

1 106 Armenia 1980 -3.0% 0.2%

1 183 Barbados 1976 -1.6% -0.8%

1 127 Belarus 1977 -4.3% -0.6%

1 154 Bosnia and Herzegovina 1978 -1.8% -0.7%

1 128 Bulgaria 1970 -3.6% -1.4%

1 71 China 1987 -1.0% 0.3%

1 155 Croatia 1950 -1.7% -0.3%

1 186 Cuba 1986 -0.9% -1.0%

1 129 Czech Republic 1967 -2.6% -0.5%

1 140 Estonia 1988 -3.7% 0.2%

1 111 Georgia 1977 -2.6% -1.0%

1 190 Guadeloupe 1989 1.1% -1.6%

1 221 Guyana 1981 -1.3% -3.4%

1 130 Hungary 1970 -1.6% -0.8%

1 84 Kazakhstan 1977 -2.5% -0.2%

1 74 Korea, Dem. Rep. 1988 0.5% -0.2%

1 146 Latvia 1979 -4.0% -0.2%

1 147 Lithuania 1983 -2.7% 0.1%

1 193 Martinique 1980 -0.6% -1.5%

1 17 Mauritius 1984 0.1% -0.5%

1 131 Poland 1973 -3.3% -0.3%

1 133 Romania 1991 -3.4% -1.1%

1 134 Russian Federation 1975 -4.4% -0.5%

1 163 Serbia and Montenegro 1998 -1.8% -0.5%

1 135 Slovakia 1997 -2.8% -0.5%

48

1 164 Slovenia 1994 -2.9% -0.7%

1 224 Suriname 1985 -0.9% -1.3%

1 166 TFYR Macedonia 1979 -1.7% -0.2%

1 103 Thailand 1990 -0.6% -0.4%

1 136 Ukraine 1976 -4.0% -0.9%

2. Countries with peak between 2000 and 2010

2 38 Algeria 2005 -1.3% 0.8%

2 213 Argentina 2030 0.3% 0.1%

2 107 Azerbaijan 2007 -1.3% 0.7%

2 46 Botswana 2008 -0.2% 0.0%

2 215 Brazil 2024 -0.3% -0.4%

2 216 Chile 2009 -0.1% 0.0%

2 205 Costa Rica 2008 0.0% -0.2%

2 188 Dominican Republic 2027 0.2% -0.2%

2 238 Fiji 2002 -0.2% -0.8%

2 191 Haiti 2030 0.0% 0.1%

2 97 Indonesia 2002 -0.3% -1.0%

2 83 Iran 2004 -2.5% 0.9%

2 192 Jamaica 2011 0.4% -0.7%

2 85 Kyrgyzstan 2008 -0.1% -0.5%

2 116 Lebanon 2012 0.4% 0.0%

2 47 Lesotho 2006 -0.8% -0.1%

2 40 Libya 2030 -1.1% 0.1%

2 209 Mexico 2012 0.2% -0.7%

2 247 Micronesia (Federated States of) 2024 -0.5% -1.3%

2 76 Mongolia 2006 -0.9% -0.6%

2 41 Morocco 2029 -0.2% 0.0%

2 100 Myanmar 2009 -0.1% -0.4%

2 132 Republic of Moldova 2002 -3.6% -0.3%

2 198 Saint Lucia 2006 -0.7% -0.1%

2 199 Saint Vincent and the Grenadines 2001 -2.2% -1.3%

2 36 Sao Tome and Principe 2029 0.5% 0.1%

2 49 South Africa 2014 0.3% -0.4%

2 89 Sri Lanka 2000 -1.1% -0.4%

Classific U.N. Country Year of

Average annual

49

growth

ation

country

code peak

2005-

2015

2025-

2035

2 50 Swaziland 2008 -0.2% 0.5%

2 259 Tonga 2004 -0.4% -1.9%

2 200 Trinidad and Tobago 2001 -3.4% -0.2%

2 43 Tunisia 2004 -1.5% -0.1%

2 122 Turkey 2020 0.4% -0.3%

2 91 Turkmenistan 2008 -0.2% 0.2%

2 92 Uzbekistan 2009 0.1% 0.0%

2 104 Viet Nam 2007 -0.5% -0.2%

2 26 Zimbabwe 2008 0.0% 0.3%

3. Countries with peak between 2010 and 2030

3 108 Bahrain 2015 1.9% -0.4%

3 80 Bangladesh 2039 1.1% 0.4%

3 204 Belize 2027 1.2% -0.1%

3 214 Bolivia 2027 1.8% -0.1%

3 29 Cameroon 2041 1.5% 0.4%

3 217 Colombia 2016 0.9% -0.1%

3 218 Ecuador 2019 0.7% -0.3%

3 39 Egypt 2031 0.3% 0.3%

3 206 El Salvador 2024 1.1% -0.1%

3 208 Honduras 2032 1.7% 0.2%

3 82 India 2024 0.8% -0.2%

3 113 Israel 2025 1.3% -0.2%

3 114 Jordan 2021 1.9% 0.0%

3 99 Malaysia 2016 1.5% -0.2%

3 237 Melanesia 2043 2.1% 0.3%

3 243 Micronesia 2026 1.1% -0.1%

3 210 Nicaragua 2030 1.1% 0.0%

3 211 Panama 2027 1.0% 0.0%

3 240 Papua New Guinea 2044 2.5% 0.4%

3 223 Peru 2033 0.7% 0.2%

3 101 Philippines 2018 1.1% -0.2%

3 251 Polynesia 2014 0.8% -0.9%

50

3 119 Qatar 2050 1.7% 0.1%

3 19 Réunion 2026 0.7% -0.2%

3 257 Samoa 2017 1.9% -2.6%

3 241 Solomon Islands 2042 2.0% 0.2%

3 42 Sudan 2049 1.9% 0.4%

3 121 Syrian Arab Republic 2030 0.5% 0.2%

3 225 Uruguay 2017 0.5% -0.5%

3 242 Vanuatu 2041 1.6% 0.4%

3 226 Venezuela 2030 0.6% 0.1%

4. Countries with peak after 2030

4 79 Afghanistan 2050 3.7% 2.8%

4 28 Angola 2050 2.7% 2.4%

4 52 Benin 2050 2.6% 1.8%

4 81 Bhutan 2050 1.4% 1.2%

4 53 Burkina Faso 2050 2.9% 2.4%

4 9 Burundi 2050 1.8% 3.5%

4 95 Cambodia 2035 0.1% 1.0%

4 54 Cape Verde 2037 0.8% 0.6%

4 30 Central African Republic 2050 1.9% 0.9%

4 31 Chad 2050 2.9% 3.0%

4 10 Comoros 2050 2.1% 0.6%

4 32 Congo 2050 3.3% 2.9%

4 55 Côte d'Ivoire 2050 1.5% 0.7%

4 33 Dem. Rep. Congo 2050 2.9% 2.7%

4 11 Djibouti 2050 1.8% 0.7%

4 96 East Timor 2050 0.9% 2.2%

4 34 Equatorial Guinea 2050 2.7% 2.1%

51

Average annual

growth

Classific

ation

U.

N.

country

code Country

Year of

peak

2005-

2015

2025-

2035

4 12 Eritrea 2050 2.9% 1.5%

4 13 Ethiopia 2050 2.6% 1.5%

4 22

0

French Guiana 2040 2.7% 0.6%

4 35 Gabon 2041 1.8% 0.5%

4 56 Gambia 2050 2.6% 0.6%

4 57 Ghana 2050 1.3% 0.6%

4 20

7

Guatemala 2038 2.4% 0.8%

4 58 Guinea 2050 2.8% 1.8%

4 59 Guinea-Bissau 2050 3.3% 2.9%

4 11

2

Iraq 2050 2.4% 0.9%

4 14 Kenya 2050 1.2% 1.4%

4 98 Laos 2042 2.0% 0.7%

4 60 Liberia 2050 2.7% 2.7%

4 15 Madagascar 2050 3.0% 1.3%

4 16 Malawi 2050 3.3% 1.8%

4 86 Maldives 2037 1.8% 0.9%

4 61 Mali 2050 3.1% 2.5%

4 62 Mauritania 2050 2.7% 1.4%

4 18 Mozambique 2050 2.4% 1.1%

4 48 Namibia 2040 2.1% 1.1%

4 87 Nepal 2043 2.0% 0.7%

4 63 Niger 2050 3.6% 2.9%

4 64 Nigeria 2050 2.2% 0.9%

4 11

8

Oman 2038 0.8% 0.9%

4 88 Pakistan 2039 1.3% 0.9%

4 11 Palestinian Territory 2050 3.6% 1.2%

52

7

4 22

2

Paraguay 2049 1.6% 0.6%

4 20 Rwanda 2050 0.9% 1.2%

4 66 Senegal 2040 1.9% 0.7%

4 67 Sierra Leone 2050 2.5% 2.2%

4 22 Somalia 2050 3.2% 1.7%

4 90 Tajikistan 2035 1.0% 0.6%

4 24 Tanzania 2039 1.8% 0.5%

4 68 Togo 2048 2.5% 1.0%

4 23 Uganda 2050 3.4% 3.7%

4 44 Western Sahara 2038 2.7% 2.3%

4 12

4

Yemen 2050 2.7% 2.0%

4 25 Zambia 2050 1.9% 1.4%

53

Appendix C. Size of Population Aged 12-24 from 1950 to 2050, United

Nations Medium Variant Projections, Classified by Categories for Timing of the

Peak 1

1.5

22.

5

11.

52

.81

1.2

1.4

.6.8

11.

2

.6.8

11.

21.

4

.4.6

.81

11.

52

2.5

.6.8

1

11.

52

2.5

.6.8

11.

21.

4

.6.8

11.

2

.6.8

11.

2

11.

52

11.

52

2.5

.6.8

11.

2

11.

52

11.

52

.6.8

11.

2

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

Albania Armenia Barbados

Belarus Bosnia and Herzegovina Bulgaria

China Croatia Cuba

Czech Republic Estonia Georgia

Guadeloupe Guyana Hungary

Kazakhstan Korea, Dem. Rep. Latvia

Pop

ulat

ion

aged

12-

24 (1

950=

1.0)

YearUnited Nations Medium Variant Projections

Category 1: Peak before 2000

54

.6.8

11.

2

11.

21.

41.

61.

8

11.

52

2.5

.81

1.2

1.4

.6.8

11.

2

.6.8

11.

2

.8.9

11.

1

.81

1.2

1.4

1.6

.6.8

11.

2

11.

52

2.5

.81

1.2

1.4

11.

52

2.5

3

.4.6

.81

1.2

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030

Lithuania Martinique Mauritius

Poland Romania Russian Federation

Serbia and Montenegro Slovakia Slovenia

Suriname TFYR Macedonia Thailand

Ukraine

Pop

ulat

ion

aged

12-

24 (1

950=

1.0)

YearUnited Nations Medium Variant Projections

Category 1: Peak before 2000

55

12

34

11.

52

2.5

11.

52

2.5

12

34

5

12

3

11.

52

2.5

12

34

5

12

34

11.

52

2.5

3

12

34

11.

52

2.5

02

46

11.

52

11.

52

2.5

3

11.

52

2.5

12

34

02

46

8

12

34

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

Algeria Argentina Azerbaijan

Botswana Brazil Chile

Costa Rica Dominican Republic Fiji

Haiti Indonesia Iran

Jamaica Kyrgyzstan Lebanon

Lesotho Libya Mexico

Pop

ulat

ion

aged

12-

24 (1

950=

1.0)

YearUnited Nations Medium Variant Projections

Category 2: Peak 2000-2010

56

12

34

12

34

12

34

11.

52

2.5

3

11.

52

11.

52

11.

52

12

34

5

12

34

11.

52

2.5

12

34

5

11.

52

2.5

11.

52

2.5

11.

52

2.5

3

11.

52

2.5

3

12

34

12

34

5

12

34

02

46

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030

Micronesia (Federated States of) Mongolia Morocco

Myanmar Republic of Moldova Saint Lucia

Saint Vincent and the Grenadines Sao Tome and Principe South Africa

Sri Lanka Swaziland Tonga

Trinidad and Tobago Tunisia Turkey

Turkmenistan Uzbekistan Viet Nam

Zimbabwe

Pop

ulat

ion

aged

12-

24 (1

950=

1.0)

YearUnited Nations Medium Variant Projections

Category 3: Peak 2000-2010

57

02

46

12

34

5

12

34

5

12

34

12

34

5

12

34

12

34

5

12

34

12

34

02

46

8

12

3

02

46

05

1015

12

34

5

12

34

12

34

02

46

12

34

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

Bahrain Bangladesh Belize

Bolivia Cameroon Colombia

Ecuador Egypt El Salvador

Honduras India Israel

Jordan Malaysia Melanesia

Micronesia Nicaragua Panama

Pop

ulat

ion

aged

12-

24 (1

950=

1.0)

YearUnited Nations Medium Variant Projections

Category 3: Peak 2010-2030

58

12

34

12

34

12

34

5

11.

52

2.5

3

010

2030

11.

52

2.5

3

11.

52

2.5

02

46

8

02

46

02

46

8

11.

21.

4

02

46

02

46

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030

Papua New Guinea Peru Philippines

Polynesia Qatar Réunion

Samoa Solomon Islands Sudan

Syrian Arab Republic Uruguay Vanuatu

Venezuela

Pop

ulat

ion

aged

12-

24 (1

950=

1.0)

YearUnited Nations Medium Variant Projections

Category 3: Peak 2010-2030

59

02

46

8

02

46

8

05

10

12

34

5

02

46

8

02

46

8

12

34

5

12

34

12

34

5

02

46

8

02

46

8

05

1015

05

10

05

10

05

1015

20

02

46

12

34

5

02

46

8

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

Afghanistan Angola Benin

Bhutan Burkina Faso Burundi

Cambodia Cape Verde Central African Republic

Chad Comoros Congo

Côte d'Ivoire Dem. Rep. Congo Djibouti

East Timor Equatorial Guinea Eritrea

Pop

ulat

ion

aged

12-

24 (1

950=

1.0)

YearUnited Nations Medium Variant Projections

Category 4: Peak after 2030

60

02

46

8

05

10

12

34

5

02

46

8

02

46

02

46

02

46

02

46

8

05

10

05

10

12

34

5

05

10

02

46

8

05

10

02

46

8

02

46

8

02

46

12

34

5

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

Ethiopia French Guiana Gabon

Gambia Ghana Guatemala

Guinea Guinea-Bissau Iraq

Kenya Laos Liberia

Madagascar Malawi Maldives

Mali Mauritania Mozambique

Pop

ulat

ion

aged

12-

24 (1

950=

1.0)

YearUnited Nations Medium Variant Projections

Category 4: Peak after 2030

61

02

46

12

34

5

05

1015

02

46

02

46

8

02

46

02

46

8

02

46

02

46

8

02

46

8

12

34

5

02

46

8

12

34

5

02

46

8

02

46

8

05

1015

010

2030

40

05

10

02

46

8

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030 1950 1970 1990 2010 2030 1950 1970 1990 2010 2030

1950 1970 1990 2010 2030

Namibia Nepal Niger

Nigeria Oman Pakistan

Palestinian Territory Paraguay Rwanda

Senegal Sierra Leone Somalia

Tajikistan Tanzania Togo

Uganda Western Sahara Yemen

Zambia

Pop

ulat

ion

aged

12-

24 (1

950=

1.0)

YearUnited Nations Medium Variant Projections

Category 4: Peak after 2030